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题名: 基于多源数据融合技术的西南生态安全屏障研究
作者: 李思远1
学位类别: 博士
答辩日期: 2017-05
授予单位: 中国科学院大学
授予地点: 北京
导师: 邓红兵 ; 董仁才
关键词: 生态安全,生态安全屏障,多源数据融合,元数据,生态本底格局 ; Ecological security, Ecological security shelter, Multi-source data fusion, Metadata, Ecological background pattern
其他题名: Study on the ecological security shelter of Southwest China based on the multi-source data fusion technique
学位专业: 生态学
中文摘要: 我国已将生态安全纳入国家安全体系,提升生态安全重要性认识、破解生态 安全威胁、构建生态安全屏障,意义重大,影响深远。西南地区是我国重要的生 态安全屏障区,同时也是生态高度脆弱的地区,存在一系列严峻的生态环境问题, 严重制约着当地社会的可持续发展。因此,开展这一区域的生态安全屏障研究具 有重要意义。 生态安全研究具有复杂性和综合性特征,空间尺度大且时间周期长,并需要 全面认知研究区内的社会-经济-自然变化特征。构建生态安全屏障,必然涉及到 大量的生态、地理、环境和人文数据。而近年来,生态安全研究的数据基础也正 发生着深刻变化。这主要体现在表征生态安全的数据的采集、获取、传输能力显 著增强,数据量急剧增加,数据类型、内容、格式日益多源化。面对多源生态数 据集,如何合理高效地组织、管理、应用和分析,以及围绕生态安全要素开展深 入的数据挖掘和数据融合,获取构建生态安全格局的关键支撑信息,是新型数据 环境下区域生态安全研究的一个重要议题。 本论文围绕西南生态安全屏障这一主题,结合空间分析技术和多源数据融合 方法,采用“明确生态本底格局-辨识生态安全格局关键区域-分析关键生态节点 区”的递进分析模式,探索基于区域社会-经济-自然复合生态系统特征的生态安 全屏障分析技术,以期为区域尺度生态安全屏障构建提供新的思路和方法。主要 研究内容包括以下四部分:(1)基于山地和河流分析生态本底格局,通过对比分 析其与生态安全格局关键区域的关联,明确区域生态本底格局在构建生态安全屏 障中的作用。并基于西南地区的自然特征,提出采用河源重要性评价来优化生态 安全格局关键区域,以此确定区域生态安全格局的主体框架;(2)通过分析西南 生态安全屏障研究的数据基础,确定应用元数据理论汇总、组织、管理生态安全 研究数据集,为后续的数据应用奠定数据基础;(3)针对研究区关键生态问题, 通过生态敏感性评价和生态系统服务重要性评价,综合得到生态安全格局关键区 域;(4)从自然要素和社会-经济要素两方面辨识维护生态安全的关键生态节点 区,包括不同尺度与管理单元下生态要素的空间分异特征、贫困县生态压力格局 和个体尺度旅游热点区的生态压力研究,继而实现西南地区生态安全屏障的初步 构建。 研究认为,我国西南地区山地和河流本身就构成了这一区域的生态屏障基础, 在构建生态安全屏障过程中,应当充分尊重、认识并利用生态本底格局;河源重 要性评价能有效优化西南生态安全格局关键区域,辅助确定西南生态安全屏障主 体框架;在区域生态安全研究数据分析过程中,尤其要注重元数据技术的应用, 以确保研究结果的合理可靠、可重复和可追溯;西南国土面积 72.25%的区域具 有极为重要的生态保护战略地位;加强贫困县生态保护工作对维护西南生态安全 具有重要意义,未来应重点关注贫困县的生态修复与保护工作;跨尺度融合特征 信息对辨识生态要素空间分异特征,构建生态安全屏障具有重要意义。
英文摘要: China has integrated ecological security (ES) into the national security system. It is important to enhance the awareness of the importance of ES, to eliminate the threats to ES, and further to establish the ecological security shelter (ESS). Southwest China is an important ESS as well as a highly ecological fragile region. There are a series of severe eco-environmental problems, which seriously restrict the local sustainable development. Therefore, it is of great significance to carry out the studies on ESS of Southwest China. The studies on ES are complex and comprehensive. The spatial scale tends to be large and the time scale tends to be long. Moreover, understanding the characteristics of social-economic-natural complex ecosystem (SENCE) in specific study region is crucial to ES studies. The construction of the ESS involves a large amount of ecological, geographic, environmental and humane data. Meantime, in recent years, the datasets relating the ES are experiencing profound changes, including remarkable improvement of the capacity of data acquisition, collection and transmission, dramatic increase of the amount of data, and the multi-source trends of data types, contents and formats. These changes challenge the effective organization, management, application and analysis of multi-source datasets. With consideration of these changes, how to conduct data mining and data fusion of ES elements and then to obtain the important supportive information for ecological security pattern (ESP) is an essential issue. This dissertation focused on how to construct the ESS of Southwest China. With the integration of the spatial analysis techniques and the multi-source data fusion methods, this study applied a progressive analysis model to explore the analysis techniques of ESS based on the characteristics of regional SENCE. The progressive analysis model included three parts, namely “clarifying the ecological background pattern (EBP) - identifying the crucial regions about the ecological security pattern (CRESP) - analyzing the key ecological node regions (KENR)”. The dissertation aimed to provide some new methodologies to construct regional ESS. The main content of this dissertation included four parts. First, this study analyzed the EBP comprised of mountains and rivers, and further investigated the relationship between EBP and CRESP to clarify the role of regional EBP in the building to ESP. Then with consideration of the natural characteristics of Southwest China, the study proposed using the importance evaluation of headwater regions to optimize the CRESP, and thus established the main pattern of regional ESP. Second, through the analysis of datasets used for the construction of ESS of Southwest China, this dissertation selected metadata theory to collect, organize and manage ES datasets. This dataset management mode was constructive to the following data application. Third, focusing on the important ecological issues of Southwest China, the study applied the ecological sensitivity evaluation and ecological services importance evaluation. And the results of these two evaluations were combined to obtain the spatial extent of CRESP. Last, the dissertation adopted the natural elements and social-economic elements to identify the KENR, and further completed the preliminary construction of ESS of Southwest China. The identification of KENR comprised three parts, namely the spatial distribution characteristics of ecological features at different scales and management units, the ecological pressure pattern of poverty county and the ecological pressure pattern of tourist hotspots that was analyzed using the individual scale dataset. The results showed that the mountains and rivers constituted the basis of ESS of Southwest China. The EBP should be sufficiently respected, understand and utilized to construct the ESS. Meantime, the importance evaluation of headwater regions can effectively optimize the CRESP and further assist in establishing the main pattern of regional ESP of Southwest China. In addition, in the process of data analysis of regions ES studies, applying the metadata theory to datasets management was essential to ensure the rational, reliable, repeatable, traceable results. The regions accounting for 72.25% of Southwest China were essential to the local ecological protection. Furthermore, the ecological restoration and protection of poverty counties were crucial to maintain the ES of Southwest China, and should be emphasized. Moreover, the cross- fusion of feature information was crucial to identify the spatial distribution characteristics of ecological element and to construct the ESS.
内容类型: 学位论文
URI标识: http://ir.rcees.ac.cn/handle/311016/38645
Appears in Collections:城市与区域生态国家重点实验室_学位论文

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